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Design, analysis and implementation of efficient deep learning frameworks for brain tumor classification

  • 1218: Engineering Tools and Applications in Medical Imaging
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Abstract

Computer-aided diagnosis (CAD) system may be utilized as assistants for doctors and radiologists for the detection of disease. CAD systems using deep learning approaches are promising in diagnosing brain tumors but due to their computationally intensive nature, they are resilient to deploy in real-time scenarios where speed, as well as accuracy, is required. Further, it is necessary for the deep-learning models to capture multi-scale information as the task brain-tumor classification requires modelling pixel-to-pixel relationship and spatial-contexts in tumor-affected regions. To this end, this paper introduces the representational feature learning powers of deep, efficient and lighter deep learning architectures based on novel weight initialization and layers freezing for brain tumor classification. We use five different weight initialization and freezing configurations, four from the domain of transfer learning and the remaining being random initialization. These configurations are applied over different parameters and memory efficient architectures. Results suggest that when architecture is initiated adequately with correct weight initialization configuration based on the number of trainable parameters and architectural depth, performance obtained is optimal. Experimentation over eight different CNN architectures and five different weight initialization configurations was conducted and therefore training and evaluation of 40 deep learning frameworks was carried out. From the comprehensive experimental analyses of classification performances over three classes of brain tumor, it is evident that DenseNet201 based transfer learning model with initial 5 convolution layers frozen attains state-of-the-art accuracy of 98.22% while the lightweight models of MobileNet outperform many other models attaining the highest 97.87% accuracy for transfer learning configuration with initial 3 convolutional layers frozen while sizing only 42.6 MBs. The DenseNet201 model utilizes densely-flowing skip connections which in-turn allows the model to utilize the features learning from different spatial-contexts to formulate understanding of features in accordance with current receptive fields. With the 5 convolutional layer frozen transfer-learning scheme the same architecture achieves a performance gain of 0.88% over the state-of-art-methods. Further, the efficacy of the random initialization paradigm for brain tumor classification is investigated, results suggest that the random initialization framework can be promising if the number of trainable parameters is kept in accordance with training data quantities.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Raipur, India

    Aman Verma

  2. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India

    Vibhav Prakash Singh

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  1. Aman Verma
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Correspondence to Vibhav Prakash Singh.

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Verma, A., Singh, V.P. Design, analysis and implementation of efficient deep learning frameworks for brain tumor classification. Multimed Tools Appl 81, 37541–37567 (2022). https://doi.org/10.1007/s11042-022-13545-0

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